Improvement of weight computational statistical modeling via the transition to a subcritical Galton-Watson process
type: Item
instance of: scholarly article
title: Improvement of weight computational statistical modeling via the transition to a subcritical Galton-Watson process
zbMATH Open document ID: 1281.60071
DOI: 10.1134/S1064562409010177
published in: Doklady Mathematics
publication date: 2012-11-15T00:00:00Z
review text: The widely used exponential transformation method in the Monte Carlo estimation is based on a modification of the exponential distribution of the particle's path with an auxiliary weight multiplied by a factor. The authors simplify this complex weighted algorithm by modifying the branching of the particle trajectory. In this way, they derive an expression for the second moment of the total number of particles in a subcritical Galton-Watson process.
Mathematics Subject Classification ID: 60J80
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 6106385
zbMATH Keywords: subcritical Galton-Watson process
zbMATH Keywords: exponential transformation method
zbMATH Keywords: Monte Carlo estimation
author: G. A. Mikhaĭlov
author: I. N. Medvedev
reviewed by: Zenghu Li
MaRDI profile type: Publication
cites work: Q3242006
cites work: Optimization of Monte Carlo weighted methods for part of variables
full work available at URL: s1064562409010177
OpenAlex ID: W2000472804
description: scientific article; zbMATH DE number 6106385